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Stan Z. Li
Researcher at Westlake University
Publications - 625
Citations - 49737
Stan Z. Li is an academic researcher from Westlake University. The author has contributed to research in topics: Facial recognition system & Computer science. The author has an hindex of 97, co-authored 532 publications receiving 41793 citations. Previous affiliations of Stan Z. Li include Microsoft & Macau University of Science and Technology.
Papers
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Journal ArticleDOI
Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study
Wenhan Yang,Ye Yuan,Wenqi Ren,Jiaying Liu,Walter J. Scheirer,Zhangyang Wang,Taiheng Zhang,Qiaoyong Zhong,Di Xie,Shiliang Pu,Yuqiang Zheng,Yanyun Qu,Yuhong Xie,Liang Chen,Zhonghao Li,Chen Hong,Hao Jiang,Siyuan Yang,Yan Liu,Xiaochao Qu,Pengfei Wan,Shuai Zheng,Minhui Zhong,Taiyi Su,Lingzhi He,Yandong Guo,Yao Zhao,Zhenfeng Zhu,Jinxiu Liang,Jingwen Wang,Tianyi Chen,Yuhui Quan,Yong Xu,Bo Liu,Xin Liu,Qi Sun,Tingyu Lin,Xiaochuan Li,Feng Lu,Lin Gu,Shengdi Zhou,Cong Cao,Shifeng Zhang,Cheng Chi,Chubing Zhuang,Zhen Lei,Stan Z. Li,Shizheng Wang,Ruizhe Liu,Dong Yi,Zheming Zuo,Jianning Chi,Huan Wang,Kai Wang,Yixiu Liu,Xingyu Gao,Zhenyu Chen,Chang Guo,Yongzhou Li,Huicai Zhong,Jing Huang,Heng Guo,Jianfei Yang,Wenjuan Liao,Jiangang Yang,Liguo Zhou,Mingyue Feng,Likun Qin +67 more
TL;DR: The UG2+ challenge Track 2 competition in IEEE CVPR 2019 is launched, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios.
Journal ArticleDOI
Regularized Discriminative Spectral Regression Method for Heterogeneous Face Matching
TL;DR: The DSR maps heterogeneous face images into a common discriminative subspace in which robust classification can be achieved, and introduces two novel regularization terms, which reflect the category relationships among data, into the least squares approach.
Journal ArticleDOI
Efficient Group-n Encoding and Decoding for Facial Age Estimation
TL;DR: An age group-n encoding (AGEn) method, in which adjacent ages are grouped into the same group and each age corresponds to n groups, which achieves the best performance against state-of-the-art methods.
Proceedings ArticleDOI
Real-time multi-view face detection
TL;DR: This work presents the first real-time multi-view face detection system which runs at 5 frames per second for 320/spl times/240 image sequence and trains by using a new meta booting learning algorithm.
Proceedings ArticleDOI
CRAFT Objects from Images
TL;DR: CRAFT as mentioned in this paper proposes a cascade region proposal-network and FasT-rcNN, which tackles each task with a carefully designed network cascade and achieves state-of-the-art performance on object detection benchmarks.